NEURO-FUZZY APPROXIMATION OF MULTI-CRITERIA DECISION-MAKING QFD METHODOLOGY

NEURO-FUZZY APPROXIMATION OF MULTI-CRITERIA DECISION-MAKING QFD METHODOLOGY

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by Ajith Abraham , Ian Vasant , Arijit Bhattacharya , Key Words

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TITLE	NEURO-FUZZY APPROXIMATION OF MULTI-CRITERIA DECISION-MAKING QFD METHODOLOGY	SVM HeaderParse 0.2
AUTHOR NAME	Ajith Abraham	SVM HeaderParse 0.2
AUTHOR AFFIL	1; Center of Excellence for Quantifiable Quality of Service, Norwegian University of Science; and Technology	SVM HeaderParse 0.2
AUTHOR ADDR	Trondheim, Norway 2 EEE Program Research Lecturer Universiti	SVM HeaderParse 0.2
AUTHOR NAME	Ian Vasant	SVM HeaderParse 0.2
AUTHOR AFFIL	1; Center of Excellence for Quantifiable Quality of Service, Norwegian University of Science; and Technology	SVM HeaderParse 0.2
AUTHOR ADDR	Trondheim, Norway 2 EEE Program Research Lecturer Universiti	SVM HeaderParse 0.2
AUTHOR NAME	Arijit Bhattacharya	SVM HeaderParse 0.2
AUTHOR AFFIL	1; Center of Excellence for Quantifiable Quality of Service, Norwegian University of Science; and Technology	SVM HeaderParse 0.2
AUTHOR ADDR	Trondheim, Norway 2 EEE Program Research Lecturer Universiti	SVM HeaderParse 0.2
AUTHOR NAME	Key Words	SVM HeaderParse 0.2
ABSTRACT	This chapter demonstrates how a neuro-fuzzy approach could produce outputs of a further-modified multi-criteria decision-making (MCDM) quality function deployment (QFD) model within the required error rate. The improved fuzzified MCDM model uses the modified S-curve membership function (MF) as stated in an earlier chapter. The smooth and flexible logistic membership function (MF) finds out fuzziness patterns in disparate level-of-satisfaction for the integrated analytic hierarchy process (AHP-QFD model. The key objective of this chapter is to guide decision makers in finding out the best candidate-alternative robot with a higher degree of satisfaction and with a lesser degree of fuzziness. ANFIS, AHP, QFD, fuzziness patterns, decision-making, level-of-satisfaction	SVM HeaderParse 0.2
CITATIONS	15 found	ParsCit 1.0